SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 776–800 of 15113 papers

TitleStatusHype
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models—0
Solving Bayesian inverse problems with diffusion priors and off-policy RL—0
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement LearningCode7
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach—0
Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving—0
Edge AI-Powered Real-Time Decision-Making for Autonomous Vehicles in Adverse Weather Conditions—0
Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning—0
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics—0
Evaluating Reinforcement Learning Safety and Trustworthiness in Cyber-Physical Systems—0
Balancing SoC in Battery Cells using Safe Action Perturbations—0
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents—0
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model—0
A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models—0
Zero-Shot Action Generalization with Limited Observations—0
HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents—0
V-Max: A Reinforcement Learning Framework for Autonomous DrivingCode2
Regulatory DNA sequence Design with Reinforcement LearningCode1
MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models—0
Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach—0
Efficient Neural Clause-Selection Reinforcement—0
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RLCode4
Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning—0
VisRL: Intention-Driven Visual Perception via Reinforced ReasoningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified